Search Results - (( java based optimization algorithm ) OR ( data computer training algorithm ))
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Major problems in classification task are large amount of training data, large number of features and different behavior of data streams that reduce accuracy and increase computational cost in classifier training phase. …”
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Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
Published 2013“…The optimization of a computationally intensive algorithm such as this on a mobile platform is challenging due to the limited resources available. …”
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3
Automatic Number Plate Recognition on android platform: With some Java code excerpts
Published 2016“…Hence, the objective of this research is to propose suitable and optimize algorithm for ANPR system on Android mobile phone. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Optimization of Multilayer Perceptron (MLP) network training algorithms for agrwood oil quality separation / Noratikah Zawani Mahabob ... [et al.]
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Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite
Published 2025“…This study proposes a combinatorial testing method utilizing the Whale Optimization Algorithm (WOA). The study compares the performance of WOA with various existing strategies, such as Greedy, Density, TVG, Union, ParaOrder, ReqOrder, ITTDG, AURA, Java Algorithm (CTJ), TTSGA, and AFA. …”
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A heuristics approach for classroom scheduling using genetic algorithm technique
Published 2017“…The proposed of heuristics approach will prompt a superior utilization of the accessible classroom space for a given time table of courses at the university. Genetic Algorithm through Java programming languages were used in this study and aims at reducing the conflicts and optimizes the fitness. …”
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Multi objective genetic algorithm for training three term backpropagation network
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Separable Recursive Training Algorithms with Switching Module
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Training functional link neural network with ant lion optimizer
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Dynamic training rate for backpropagation learning algorithm
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Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm
Published 2020“…It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. …”
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Semantic-k-NN algorithm: An enhanced version of traditional k-NN algorithm
Published 2020“…It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. …”
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Development of Machine Learning Algorithm for Acquiring Machining Data in Turning Process
Published 2004“…The design network is trained by presenting several target machining data that the network must learn according to a learning rule (algorithm). …”
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Integrating genetic algorithms and fuzzy c-means for anomaly detection
Published 2005“…Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. …”
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